The present invention generally pertains to systems and methods for automatic pattern recognition. More specifically, the present invention relates to using a speech recognition system to recognize a personal alias.
An alias is a string of letters numbers and/or symbols that comprise an alternate name of a user or an email address of a user. Aliases are used for interacting with a computer network. User aliases or personal aliases generally contain at least portions of a user's first name, middle name and/or last name. For example, an alias can be a username portion of an email address.
Aliases were designed to be entered into a computing device using a keyboard. Aliases were never intended to be spoken in the natural language. Thus, if a user has a first name John and a last name Doe, then a possible alias for John Doe could be entered into a computing device as “jdoe”. This example alias includes a portion of the user's first name and the user's full last name without any spaces between the portion of the first name and the full last name.
Automatic speech recognition systems transcribe voice into text using a pronunciation dictionary that spells out textual representations into phonemes. To accommodate out of dictionary vocabulary, such as acronyms and jargon, a letter-to-sound (LTS) subsystem is often included to account for words that are not in the dictionary. However, current LTS subsystems are designed to map orthography into phonemes. For example, if a user were to naturally speak the example alias “jdoe”, a LTS subsystem would spell out the alias as “jay doe”. To force a LTS subsystem to correctly spell out the alias “jdoe”, a user would have to phonetically pronounce the “j” in combination with “doe” by not pronouncing the “j” as a letter. However, this phonetic pronunciation of an alias is unnatural and confusing.
Automatic speech recognition systems can also transcribe vocally spelled letters into text. However, automatic speech recognition systems have problems understanding individual letters that sound similar. For example, the letters “d”, “e” and “v” all sound similar. Some automatic speech recognition systems employ ways to differentiate between similar sounding letters. For example, the systems require the speaker to say “v as in victor”. However, this is a rather tedious way of entering text.